Ministral 3 8B needs ~10.3 GB but RTX 4060 Laptop 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
2.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
15.0 tok/s
TTFT
12926 ms
Safe context
4K
Memory
10.3 GB / 8.0 GB
Offload
20%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 10.3 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 19.0 tok/s | 5557 ms | 4K |
| Coding | F | Too heavy | 15.0 tok/s | 12926 ms | 4K |
| Agentic Coding | F | Too heavy | 10.0 tok/s | 28270 ms | 4K |
| Reasoning | F | Too heavy | 15.0 tok/s | 15276 ms | 4K |
| RAG | F | Too heavy | 10.0 tok/s | 35337 ms | 4K |
How Ministral 3 8B (8B params) fits at each quantization level on RTX 4060 Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A84 |
Q3_K_S | 3 | 3.9 GB | Low | A84 |
NVFP4 | 4 |
Upgrade options
RTX 3060 12GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$329 MSRP
RTX 5060 Ti 16GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$499 MSRP
No, Ministral 3 8B requires more memory than RTX 4060 Laptop 8GB provides.
Ministral 3 8B (8B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 3 8B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4060 Laptop 8GB, Ministral 3 8B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12926ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on RTX 4060 Laptop 8GB receives a F grade with 15.0 tok/s and 4K context.
On RTX 4060 Laptop 8GB, Ministral 3 8B can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/ministral-3-8b-on-rtx-4060-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A84 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | A83 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
